Abstract: This paper presents the implementation on a healthcare dataset using data mining tools to find important parameters that reflect the effect of diabetes on kidney of patients. This is done with the use of Kidney Function Tests (KFT). The data mining tools used are Tanagra and Weka with the application of C4.5 Algorithm which is based on decision trees. This paper compares the result given by Weka and Tanagra. The outcome of both the tools is analyzed and conclusion is drawn that both the tools are able to work well on dataset but Tanagra is more efficient and less error-prone in terms of the performance of the classifier. The effective usage of data mining tools enables us to find important parameters that reflect the effect of diabetes on kidney. Additionally, it is found that the performance of Weka is best when used with Use Training Set mode than with cross validation followed by percentage split mode for training the classifier.
Keywords: Weka, Tanagra, Diabetes, Classification, Kidney